Launch

It’s another big day in the life of Iris.ai, and we couldn’t be prouder parents, with the official launch of version 4.0. Last year brought our free users the option to create accounts, bookmark useful content, and revisit their browsing history. This time, we’ve introduced a new premium feature: The Focus Tool.

When conducting preliminary research on a topic, it’s easy to amass a list of hundreds, if not thousands of relevant research papers, especially with the help of Iris.ai. The Focus tool makes it easier to distill that list into a precise and—well, we’ll go ahead and say it—focused list of the most relevant articles to you.

While Iris.ai’s Exploration algorithm is great at surfacing a comprehensive landscape of interdisciplinary papers that relate to your original article or question, the Focus Tool allows the creation of intelligent filters to include or exclude topics of interest, retraining the algorithm as it goes.

This significantly reduces the average time it takes for professional researchers to compile a full report of relevant papers to support their work, in turn reducing a process that could take weeks to as little as two days and increases the confidence level of results by 15%.

But that’s not all…

Premium users will also find that they have a new way to search within the Exploration tool. You can now manually enter your research question and problem statement to generate a map, without the need to provide existing research on the topic.

Ready to see it in action?

Check out our latest video showing the new 4.0 tools and how they work together to get you a concise and manageable reading list in record time.

Interested in what premium can do for you or your organization? Visit https://iris.ai/customers/ or e-mail maria@iris.ai for more information.

We’re happy to announce our baby Iris.ai has taken another little step – this time bringing our free users some new useful features we believe will make your lives a bit easier.

The obvious goodies – history and bookmarks in your own account

You know that feeling as you move through a map you’ve created, and you find just the paper you need – but just want to save it to read for later? We have you covered – bookmark it and you will find it again to download directly in the reading list in your dashboard. You can also save full maps, when you create a good one.

Researching a problem is a process, and we realize keeping track of where you’ve been and what you’ve seen isn’t always the easiest. So we do it for you; you can now visit your dashboard to review all the maps you’ve created and all the papers you’ve opened. This might come in handy if you suddenly remember something you saw earlier but didn’t realize at the time that it was relevant; we’ve now got you covered so you can retrace your steps from your dashboard.

There’s always more than meets the eye in these releases, and Iris.ai 3.0 doesn’t only come with new front end features but she’s gotten a solid algorithm upgrade too. New and improved data models, better tuning, some new neural network algorithms replacing older off-the-shelf components; we’re quite pleased with the results and while not as visible as the frontend we hope you will be, too.

Our new training tool is OUT. Starting today, the human friends of Iris.AI can train the AI directly from research paper abstracts and help her understand synonyms.

In May 2016 we launched the first version of our AI training tool to help Iris.AI learn via Ted Talks. A large labelled data set was needed to build an effective training loop. Since then we’ve asked our users to join us in our effort to take the existing stock of research into effective use by participating in this learning experiment.

Fast-forward 10 months and the crowd-training has become one of the backbones of our AI development. Iris.AI trainers around the world have done a tremendous job by training thousands of texts, equivalent to more than half a million trained concepts. These inputs have allowed us not just to improve the accuracy of our algorithm by several percentages but also to verify and assess the quality of it.

Today we’re taking the AI training to new heights by releasing the next version of our training tool. The new curriculum allows Iris.AI to learn directly from research papers and synonyms. This means that the source data of the supervised data set expands to millions of texts letting Iris.ai optimise her neural nets with scientific concepts across research fields.

Our next goal is to gather and inject a trained dataset of 5000 paper abstracts to the algorithm. With those inputs we aim to improve the connections in the neural nets of Iris.AI by approximately 10 %.

Interested in joining the effort and taking Iris.AI to the next grade? Sign up to become a trainer and we’ll help you get started.

Kudos to all our existing AI trainers and welcome new ones! We look forward to sharing the next leaps of this journey with you.

We are thrilled and excited to announce that Iris.AI v2.0, the newest version of Iris.AI, is live!

When we started defining Iris.AI in October 2015, we knew what we had set out to do would be complicated and take time. We agreed that we wanted to launch something within 3 months, and so the 1.0 version (as we know it) was born. But of course, this was only the tiny beginning and since then we’ve been heads down; building, fundraising, discussing, talking to users, laughing, and no startup dream would be complete without a lot of swearing and occasionally crying.

We expanded our tech team with a few more amazing people. We built an AI trainer platform and community with hundreds of people helping Iris.AI learn. We started a collaboration with a great design firm. We found some great people who believe in us. We found active and eager pilot customers. We evaluated algorithms, libraries, approaches, methodologies and tools. We made hard choices and prioritized. And we coded. We coded A LOT.

And here she is! Our baby Iris.AI, our labor of love, has become a toddler. Still with much to learn, but this time we believe she has something to teach us, too. Let us introduce you to the changes you’ll find when you start exploring.

Brand new UX. While you’ll recognize the functionalities and visual elements, the overall design of Iris.AI has seen a major upgrade – and is the instantly most visible change.

Input data – for real this time. While the TED talks were fun and engaging, they were just the tiny beginning. You can now start with any research paper in the world. Iris.AI will work with the abstract and present you with a context-based science map.

Output data – 15x more. Iris.AI now helps you navigate more than 30 million Open Access research papers.

New engine under the hood.

Neural network. New as of this version is that we now do our topic modeling in a neural net. Currently, this has given us about 7% improved results – and it will continue to improve as the tool is being used.

Supervised learning. With major thanks to a heroic effort to our AI trainers, that have trained Iris.AI on more than 1000 TED talks for us, we are incorporating a layer of this supervised training. This has improved the results with around 5%, for now.

Opportunities for pilots with custom content. We now have the ability to train Iris.AI on specific scientific content, and have opened up for a limited number of pilot customers to join us.

I could go on forever about how excited we are, but I’d rather you go check Iris.AI out for yourself. And let us know what you think, if you find any bugs or if you have suggestions, will you? (founders@iris.ai)

How to build a rocket with composite materials? Together with the leading European research institute Swerea SICOMP and Chalmers University we organised a science hackathon, or scithon, as we call it. The goal of the 4-hour sprint was to map out solutions to this space challenge and, in the process, get a grasp of where Iris.AI 2.0* stands compared to traditional science discovery tools.

On September 20th two teams of cross-disciplinary Masters and Ph.D. students from fields spanning from mechanical engineering and industrial design to computer science, astrophysics and entrepreneurship were handed a research challenge: Is it possible to make a reusable rocket made completely out of composite materials?

This challenge provided by Swerea SICOMP is particularly difficult due to issues like the performance of composites at extreme temperatures, the limited durability against UV and space radiation, chemical resistance issues with rocket fuels, and oxidation in high concentrations of oxygen.

After introducing the challenge and the rules of the game, the teams were pitted against each other. They both had four hours to achieve two goals: (1) map and categorise related scientific articles; and, (2) summarise the key findings by skimming through the categories and papers. Only one of the teams had access to Iris.AI.

The specific criteria they would be evaluated on were the relevance, breadth and completeness of the research papers identified. Teams’ work was also assessed based on the quality of the conclusions drawn, including elements like issues surfaced, key trends and current research directions identified.

After the sprint, an expert panel evaluated the results obtained by both teams. Team 2, using Iris.AI as the tool, generated a score of 95%.Team 1, using the current market standard product, scored 45%.

The number of generally relevant papers identified was similar for both teams. The different angles covered by these papers (with categories like validation research vs. evaluation research vs. solution proposals vs. philosophical papers vs. opinion papers vs. experience papers) was broadly similar, too.

The scithon jury attributed a significantly higher score to the Team 2, i.e. the team that had used Iris.AI, on three accounts: (1) finding three papers with a top score in terms of fitting the problem statement; (2) showing higher quality of key findings structured around identified topics; and, (3) drawing superior conclusions and summarising the relevant knowledge.

While the team using an existing market standard tool struggled to formulate the relevant keywords to optimise their searches, i.e. facing issues around dated terminology, members of the jury from our co-organisers Swerea SICOMP were particularly impressed by the papers identified by the team using Iris.AI. More specifically, the team using Iris.AI found papers around silicon-based nanoparticles and a distributed health monitoring system for reusable liquid rocket engines. These two key avenues of research could bring us a lot closer to building reusable rockets made of composite materials!

This means that version 2.0 of Iris.AI, with its full text search, unbiased mathematical architecture, neural topic modelling and visual navigation interface features, is beginning to show significant value added for researchers looking to speed up the effective discovery and deployment of scientific research.

The scithon also allowed us to gather invaluable feedback from researchers around the importance of features like filtering (including search criteria refinements) and interaction (including discarding concepts presented by Iris.AI in results maps), which will be included in our near term product roadmap.

The next scithon will be organised on October 28th in Stockholm in collaboration with Iris.AI and Future Earth. If you are in the area and would like to join us to identify solutions to climate change, contact Maria at maria@iris.ai.

*The new version of Iris.AI will be launched on the 22nd of September. Be the first one to hear about it by signing up to our newsletter at www.iris.ai.

Interested in having a look at the Scithon material? Here’s the Dropbox link to view the results delivered by both teams as well as the full version of the problem statement.

In case you’d like to be part of this learning experiment and become her teacher, sign up, but do it quickly as we only have a limited number spots for AI teachers for the next few months.

AI TRAINING FAQ

What is this AI training about again?

It’s a huge learning experiment where we ask our volunteering AI trainers to help Iris.AI grasp the meaning of what she has read.

Why does an AI need human teachers?

Imagine yourself in a lecture you know nothing about in advance. You’ll understand some of the content, but miss some as well, for sure. Then, later on you’ll pick up the remaining bits and pieces by reading more and asking your peers what they think about the topic. The learning process of an AI is very much like that. She can’t figure out the world by herself. She needs your help!

What does AI training mean in practice?

The very first version of our AI training platform is built around the TED talks as that’s what Iris.AI has read thus far. On the training platform you’ll be asked to watch any TED talk you like and validate the concepts Iris.AI has extracted from that talk. Easier said, we ask you to tell us if Iris.AI got it or not.

Do I need to be a researcher or know something about artificial intelligence in order to teach?

Absolutely not.

Ok, how can I get started?

Sign up here and we’ll provide you with your login credentials via email.

When will I see the results of training?

With thousands of inputs from our AI trainers we’ll be able to optimise the artificial brain of Iris.AI. Through this process certain concepts start to get more emphasis while new concepts are emerging. The algorithm won’t change immediately after trainers’ inputs, though, as it takes some time to gather enough data. In some months, with that data, our AI scientist’s brain starts to mimic the thinking of her teachers.

This first version of our AI lets you deep-dive into the science behind any TED-talk. TED lovers are now be able to explore relevant content related to the talks, machine-selected from more than two million open access research papers. You can navigate the content from in-depth detail up to a big picture overview covering a multitude of research fields.

Iris.ai is improving everyday, and although we humbly note that there’s a huge scope for improvement, we are pretty fascinated by the results. Here’s an example talk by Aaron O’Connell that Iris.ai reads particularly well:

2. Why TED?

Because we love the talks and we’ve always been curious to explore the science around them. Now there’s a tool dedicated to that. 🙂

Because mastering 2,000+ TED Talks works as a great starting point for Iris.AI to learn. The talks provide Iris.AI with an overall understanding of a wide number of different research fields.

Because we are starting our AI Fellowship Program and know that there are many TED fans who would like to be part of that.

3. Why we do this?

A huge amount of new research is being published everyday by brilliant researchers around the world. Yet, only a disturbingly small portion of it is put into practice or ever even read. Research papers, and the connections they make, hold great potential to solve humanity’s biggest problems from climate change to curing cancer. The challenge is that our human brains are not powerful enough to process all the information that is available.

Our ultimate goal is to build an artificial intelligence capable of highlighting new trends and interconnections of discoveries to unleash the disruptive power of science and help us connect the dots.

4. What happens next?

For now Iris.ai relies fully on computer intelligence and 2,000+ TED-talks that we gave her to read. In the future, she needs to learn a lot more to master all the research fields and to provide stellar results.

AI is a complex field where these results can be best achieved by combining human and computer intelligence. Therefore, our next steps include broadening our input and output ranges as well as developing our AI by training it with individuals, companies and institutions with a passion for making science more accessible.

We already have a number of universities and research institutions doing this with us including Future Earth, Stockholm Resilience Centre, Chalmer’s university and Aalto University.

Wish to be part of this community, too? Join our AI Fellowship Program and you’ll hear back from us very soon.

As huge fans of TED, we drew inspiration from the talks to build the very first version of our tool.

This tool lets you deep-dive into the science behind any TED-talk that gives you goosebumps. You’ll be able to explore cutting-edge research related to the talk – from in-depth detail up to a big picture overview covering a multitude of research fields.

In other words, you’ll get to see what our constantly developing AI thinks about science. It is improving every day, and although we humbly note that there’s huge scope for improvement, we are pretty fascinated by the results. Can’t wait to hear your thoughts about it!